Update README.md

README.md

@@ -8,14 +8,30 @@ tags:
 language:
 - en
 ---
-#  BERT base model (uncased)
-
+#  BERT Base Intent model
+This is a fine tuned model based on Bert-Base-Uncased model. This is model is used to classify intent into 3 categories- Fintech, Out of Scope and Abusive.
 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
 [this paper](https://arxiv.org/abs/1810.04805) and first released in
 [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
 between english and English.
 
-## Model Details
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-5
+- num_epochs: 3
+- weight_decay:0.01
+
+### Training results
+
+| Training Loss | Epoch |  Validation Loss | Accuracy        | F1       |
+|:-------------:|:-----:|:----------------:|:---------------:|:--------:|
+| 0.114200      | 1.0   | 0.034498         | 0.991351        | 0.991346 |
+| 0.024100      | 2.0   | 0.037945         | 0.992349        | 0.992355 |
+| 0.009800      | 3.0   | 0.034846         | 0.993347        | 0.993345 |
+
 
 ### Model Description
 
@@ -37,9 +53,8 @@ This way, the model learns an inner representation of the English language that
 useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard
 classifier using the features produced by the BERT model as inputs.
 
-- **Developed by:** [Jeswin MS, Venkatesh R, Kushal S Ballari]
-- **Model type:** [Intent Classification]
-- **Language(s) (NLP):** [English]
-- **License:** []
-- **Finetuned from model [optional]:** [Bert-base-uncased]
+- **Developed by:** Jeswin MS, Venkatesh R, Kushal S Ballari
+- **Model type:** Intent Classification
+- **Language(s) (NLP):** English
+- **Finetuned from model:** Bert-base-uncased